{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入数据集合\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Train MSE: 570.36755\n",
      "100 Train MSE: 17.716026\n",
      "200 Train MSE: 16.092857\n",
      "300 Train MSE: 15.40169\n",
      "400 Train MSE: 14.823234\n",
      "500 Train MSE: 14.175096\n",
      "600 Train MSE: 13.79578\n",
      "700 Train MSE: 13.642618\n",
      "800 Train MSE: 13.560842\n",
      "900 Train MSE: 13.507963\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277d17bef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding:utf-8\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.datasets import load_iris, load_digits\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "'''\n",
    "author: heucoder\n",
    "email: 812860165@qq.com\n",
    "date: 2019.6.13\n",
    "'''\n",
    "\n",
    "def reset_graph(seed=42):\n",
    "    '''\n",
    "    reset deafault graph\n",
    "    :param seed: random seed\n",
    "    :return:\n",
    "    '''\n",
    "    tf.reset_default_graph()\n",
    "    tf.set_random_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "def AutoEncoder(data,\n",
    "                hidden_layers = None,\n",
    "                noise = 0,\n",
    "                drop_rate = 0,\n",
    "                n_epochs = 301,\n",
    "                learning_rate = 0.01,\n",
    "                optimizer_type = 'adam',\n",
    "                verbose = 1):\n",
    "    '''\n",
    "\n",
    "    :param data: (n_samples, n_features)\n",
    "    :param hidden_layers: list hidden layers units num\n",
    "    :param noise: normal noise\n",
    "    :param drop_rate:\n",
    "    :param n_epochs:\n",
    "    :param learning_rate:\n",
    "    :param optimizer_type:\n",
    "    :param verbose:\n",
    "    :return:\n",
    "    '''\n",
    "\n",
    "\n",
    "    reset_graph()\n",
    "    n_inputs = data.shape[1]\n",
    "    n_outputs = n_inputs\n",
    "\n",
    "    X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "\n",
    "    # add noise\n",
    "    X_noise = X + noise * tf.random_normal(tf.shape(X))\n",
    "\n",
    "    # dropout\n",
    "    training = tf.placeholder_with_default(False, shape=(), name = \"training\")\n",
    "    X_drop = tf.layers.dropout(X_noise, drop_rate, training=training)\n",
    "\n",
    "    hiddens = [X_drop]\n",
    "    for i in range(len(hidden_layers)):\n",
    "        n_layer = hidden_layers[i]\n",
    "        hidden = tf.layers.dense(hiddens[i], n_layer, )\n",
    "        hiddens.append(hidden)\n",
    "\n",
    "    outputs = tf.layers.dense(hiddens[-1], n_outputs)\n",
    "    hiddens.append(outputs)\n",
    "\n",
    "    reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n",
    "\n",
    "    if optimizer_type == 'adam':\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "    else:\n",
    "        optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n",
    "\n",
    "    training_op = optimizer.minimize(reconstruction_loss)\n",
    "\n",
    "    init = tf.global_variables_initializer()\n",
    "\n",
    "    # coding layer\n",
    "    codings = hiddens[len(hiddens)//2]\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        init.run()\n",
    "        for epoch in range(n_epochs):\n",
    "            sess.run(training_op, feed_dict={X: data, training: True})\n",
    "            loss_train = reconstruction_loss.eval(feed_dict={X: data})\n",
    "            if epoch % 100 == 0 and verbose:\n",
    "                print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
    "        data_ndim = codings.eval(feed_dict={X: data})\n",
    "\n",
    "    return data_ndim\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    iris = load_digits()\n",
    "    X = iris.data\n",
    "    Y = iris.target\n",
    "    data_1 = AutoEncoder(X, [2], learning_rate = 0.2,  n_epochs = 1000)\n",
    "\n",
    "    data_2 = PCA(n_components=2).fit_transform(X)\n",
    "\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.subplot(121)\n",
    "    plt.title(\"my_AutoEncoder\")\n",
    "    plt.scatter(data_1[:, 0], data_1[:, 1], c = Y)\n",
    "\n",
    "    plt.subplot(122)\n",
    "    plt.title(\"sklearn_PCA\")\n",
    "    plt.scatter(data_2[:, 0], data_2[:, 1], c = Y)\n",
    "    plt.savefig(\"AutoEncoder.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# coding:utf-8\n",
    "# 代码来源:https://blog.csdn.net/lizhe_dashuju/article/details/50263339\n",
    "\n",
    "\n",
    "# FastICA\n",
    "import math\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import *\n",
    "\n",
    "n_components = 2\n",
    "\n",
    "def f1(x, period = 4):\n",
    "    return 0.5*(x-math.floor(x/period)*period)\n",
    "\n",
    "def create_data():\n",
    "    #data number\n",
    "    n = 500\n",
    "    #data time\n",
    "    T = [0.1*xi for xi in range(0, n)]\n",
    "    #source\n",
    "    S = array([[sin(xi)  for xi in T], [f1(xi) for xi in T]], float32)\n",
    "    #mix matrix\n",
    "    A = array([[0.8, 0.2], [-0.3, -0.7]], float32)\n",
    "    return T, S, dot(A, S)\n",
    "\n",
    "def whiten(X):\n",
    "    #zero mean\n",
    "    X_mean = X.mean(axis=-1)\n",
    "    X -= X_mean[:, newaxis]\n",
    "    #whiten\n",
    "    A = dot(X, X.transpose())\n",
    "    D , E = linalg.eig(A)\n",
    "    D2 = linalg.inv(array([[D[0], 0.0], [0.0, D[1]]], float32))\n",
    "    D2[0,0] = sqrt(D2[0,0]); D2[1,1] = sqrt(D2[1,1])\n",
    "    V = dot(D2, E.transpose())\n",
    "    return dot(V, X), V\n",
    "\n",
    "def _logcosh(x, fun_args=None, alpha = 1):\n",
    "    gx = tanh(alpha * x, x); g_x = gx ** 2; g_x -= 1.; g_x *= -alpha\n",
    "    return gx, g_x.mean(axis=-1)\n",
    "\n",
    "def do_decorrelation(W):\n",
    "    #black magic\n",
    "    s, u = linalg.eigh(dot(W, W.T))\n",
    "    return dot(dot(u * (1. / sqrt(s)), u.T), W)\n",
    "\n",
    "def do_fastica(X):\n",
    "    n, m = X.shape; p = float(m); g = _logcosh\n",
    "    #black magic\n",
    "    X *= sqrt(X.shape[1])\n",
    "    #create w\n",
    "    W = ones((n,n), float32)\n",
    "    for i in range(n): \n",
    "        for j in range(i):\n",
    "            W[i,j] = random.random()\n",
    "\n",
    "    #compute W\n",
    "    maxIter = 200\n",
    "    for ii in range(maxIter):\n",
    "        gwtx, g_wtx = g(dot(W, X))\n",
    "        W1 = do_decorrelation(dot(gwtx, X.T) / p - g_wtx[:, newaxis] * W)\n",
    "        lim = max( abs(abs(diag(dot(W1, W.T))) - 1) )\n",
    "        W = W1\n",
    "        if lim < 0.0001:\n",
    "            break\n",
    "    return W\n",
    "\n",
    "def show_data(T, S):\n",
    "    plt.plot(T, [S[0,i] for i in range(S.shape[1])], marker=\"*\")\n",
    "    plt.plot(T, [S[1,i] for i in range(S.shape[1])], marker=\"o\")\n",
    "    plt.show()\n",
    "\n",
    "def main():\n",
    "    T, S, D = create_data()\n",
    "    Dwhiten, K = whiten(D)\n",
    "    W = do_fastica(Dwhiten)\n",
    "    #Sr: reconstructed source\n",
    "    Sr = dot(dot(W, K), D)\n",
    "    show_data(T, D)\n",
    "    show_data(T, S)\n",
    "    show_data(T, Sr)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ISOMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding:utf-8\n",
    "import numpy as np\n",
    "from sklearn.datasets import make_s_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import Isomap\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def floyd(D,n_neighbors=15):\n",
    "    Max = np.max(D)*1000\n",
    "    n1,n2 = D.shape\n",
    "    k = n_neighbors\n",
    "    D1 = np.ones((n1,n1))*Max\n",
    "    D_arg = np.argsort(D,axis=1)\n",
    "    for i in range(n1):\n",
    "        D1[i,D_arg[i,0:k+1]] = D[i,D_arg[i,0:k+1]]\n",
    "    for k in range(n1):\n",
    "        for i in range(n1):\n",
    "            for j in range(n1):\n",
    "                if D1[i,k]+D1[k,j]<D1[i,j]:\n",
    "                    D1[i,j] = D1[i,k]+D1[k,j]\n",
    "    return D1\n",
    "\n",
    "def cal_pairwise_dist(x):\n",
    "    '''计算pairwise 距离, x是matrix\n",
    "    (a-b)^2 = a^2 + b^2 - 2*a*b\n",
    "    '''\n",
    "    sum_x = np.sum(np.square(x), 1)\n",
    "    dist = np.add(np.add(-2 * np.dot(x, x.T), sum_x).T, sum_x)\n",
    "    #返回任意两个点之间距离的平方\n",
    "    return dist\n",
    "\n",
    "def my_mds(dist, n_dims):\n",
    "    # dist (n_samples, n_samples)\n",
    "    dist = dist**2\n",
    "    n = dist.shape[0]\n",
    "    T1 = np.ones((n,n))*np.sum(dist)/n**2\n",
    "    T2 = np.sum(dist, axis = 1)/n\n",
    "    T3 = np.sum(dist, axis = 0)/n\n",
    "\n",
    "    B = -(T1 - T2 - T3 + dist)/2\n",
    "\n",
    "    eig_val, eig_vector = np.linalg.eig(B)\n",
    "    index_ = np.argsort(-eig_val)[:n_dims]\n",
    "    picked_eig_val = eig_val[index_].real\n",
    "    picked_eig_vector = eig_vector[:, index_]\n",
    "\n",
    "    return picked_eig_vector*picked_eig_val**(0.5)\n",
    "\n",
    "def my_Isomap(data,n=2,n_neighbors=30):\n",
    "    D = cal_pairwise_dist(data)**0.5\n",
    "    D_floyd=floyd(D, n_neighbors)\n",
    "    data_n = my_mds(D_floyd, n_dims=n)\n",
    "    return data_n\n",
    "\n",
    "def scatter_3d(X, y):\n",
    "    fig = plt.figure(figsize=(6, 5))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.hot)\n",
    "    ax.view_init(10, -70)\n",
    "    ax.set_xlabel(\"$x_1$\", fontsize=18)\n",
    "    ax.set_ylabel(\"$x_2$\", fontsize=18)\n",
    "    ax.set_zlabel(\"$x_3$\", fontsize=18)\n",
    "    plt.show()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    X, Y = make_s_curve(n_samples = 500,\n",
    "                           noise = 0.1,\n",
    "                           random_state = 42)\n",
    "\n",
    "    data_1 = my_Isomap(X, 2, 10)\n",
    "\n",
    "    data_2 = Isomap(n_neighbors = 10, n_components = 2).fit_transform(X)\n",
    "\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.subplot(121)\n",
    "    plt.title(\"my_Isomap\")\n",
    "    plt.scatter(data_1[:, 0], data_1[:, 1], c = Y)\n",
    "\n",
    "    plt.subplot(122)\n",
    "    plt.title(\"sklearn_Isomap\")\n",
    "    plt.scatter(data_2[:, 0], data_2[:, 1], c = Y)\n",
    "    plt.savefig(\"Isomap.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#coding:utf-8\n",
    "import numpy as np\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.datasets import load_iris\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "'''\n",
    "author: heucoder\n",
    "email: 812860165@qq.com\n",
    "date: 2019.6.13\n",
    "'''\n",
    "\n",
    "\n",
    "def lda(data, target, n_dim):\n",
    "    '''\n",
    "    :param data: (n_samples, n_features)\n",
    "    :param target: data class\n",
    "    :param n_dim: target dimension\n",
    "    :return: (n_samples, n_dims)\n",
    "    '''\n",
    "\n",
    "    clusters = np.unique(target)\n",
    "\n",
    "    if n_dim > len(clusters)-1:\n",
    "        print(\"K is too much\")\n",
    "        print(\"please input again\")\n",
    "        exit(0)\n",
    "\n",
    "    #within_class scatter matrix\n",
    "    Sw = np.zeros((data.shape[1],data.shape[1]))\n",
    "    for i in clusters:\n",
    "        datai = data[target == i]\n",
    "        datai = datai-datai.mean(0)\n",
    "        Swi = np.mat(datai).T*np.mat(datai)\n",
    "        Sw += Swi\n",
    "\n",
    "    #between_class scatter matrix\n",
    "    SB = np.zeros((data.shape[1],data.shape[1]))\n",
    "    u = data.mean(0)  #所有样本的平均值\n",
    "    for i in clusters:\n",
    "        Ni = data[target == i].shape[0]\n",
    "        ui = data[target == i].mean(0)  #某个类别的平均值\n",
    "        SBi = Ni*np.mat(ui - u).T*np.mat(ui - u)\n",
    "        SB += SBi\n",
    "    S = np.linalg.inv(Sw)*SB\n",
    "    eigVals,eigVects = np.linalg.eig(S)  #求特征值，特征向量\n",
    "    eigValInd = np.argsort(eigVals)\n",
    "    eigValInd = eigValInd[:(-n_dim-1):-1]\n",
    "    w = eigVects[:,eigValInd]\n",
    "    data_ndim = np.dot(data, w)\n",
    "\n",
    "    return data_ndim\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    iris = load_iris()\n",
    "    X = iris.data\n",
    "    Y = iris.target\n",
    "    data_1 = lda(X, Y, 2)\n",
    "\n",
    "    data_2 = LinearDiscriminantAnalysis(n_components=2).fit_transform(X, Y)\n",
    "\n",
    "\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.subplot(121)\n",
    "    plt.title(\"my_LDA\")\n",
    "    plt.scatter(data_1[:, 0], data_1[:, 1], c = Y)\n",
    "\n",
    "    plt.subplot(122)\n",
    "    plt.title(\"sklearn_LDA\")\n",
    "    plt.scatter(data_2[:, 0], data_2[:, 1], c = Y)\n",
    "    plt.savefig(\"LDA.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# LE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# coding:utf-8\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "'''\n",
    "author: heucoder\n",
    "email: 812860165@qq.com\n",
    "date: 2019.6.13\n",
    "'''\n",
    "\n",
    "def make_swiss_roll(n_samples=100, noise=0.0, random_state=None):\n",
    "    #Generate a swiss roll dataset.\n",
    "    t = 1.5 * np.pi * (1 + 2 * np.random.rand(1, n_samples))\n",
    "    x = t * np.cos(t)\n",
    "    y = 83 * np.random.rand(1, n_samples)\n",
    "    z = t * np.sin(t)\n",
    "    X = np.concatenate((x, y, z))\n",
    "    X += noise * np.random.randn(3, n_samples)\n",
    "    X = X.T\n",
    "    t = np.squeeze(t)\n",
    "    return X, t\n",
    "\n",
    "def rbf(dist, t = 1.0):\n",
    "    '''\n",
    "    rbf kernel function\n",
    "    '''\n",
    "    return np.exp(-(dist/t))\n",
    "\n",
    "def cal_pairwise_dist(x):\n",
    "\n",
    "    '''计算pairwise 距离, x是matrix\n",
    "    (a-b)^2 = a^2 + b^2 - 2*a*b\n",
    "    '''\n",
    "    sum_x = np.sum(np.square(x), 1)\n",
    "    dist = np.add(np.add(-2 * np.dot(x, x.T), sum_x).T, sum_x)\n",
    "    #返回任意两个点之间距离的平方\n",
    "    return dist\n",
    "\n",
    "def cal_rbf_dist(data, n_neighbors = 10, t = 1):\n",
    "\n",
    "    dist = cal_pairwise_dist(data)\n",
    "    n = dist.shape[0]\n",
    "    rbf_dist = rbf(dist, t)\n",
    "\n",
    "    W = np.zeros((n, n))\n",
    "    for i in range(n):\n",
    "        index_ = np.argsort(dist[i])[1:1+n_neighbors]\n",
    "        W[i, index_] = rbf_dist[i, index_]\n",
    "        W[index_, i] = rbf_dist[index_, i]\n",
    "\n",
    "    return W\n",
    "\n",
    "def le(data,\n",
    "          n_dims = 2,\n",
    "          n_neighbors = 5, t = 1.0):\n",
    "    '''\n",
    "\n",
    "    :param data: (n_samples, n_features)\n",
    "    :param n_dims: target dim\n",
    "    :param n_neighbors: k nearest neighbors\n",
    "    :param t: a param for rbf\n",
    "    :return:\n",
    "    '''\n",
    "    N = data.shape[0]\n",
    "    W = cal_rbf_dist(data, n_neighbors, t)\n",
    "    D = np.zeros_like(W)\n",
    "    for i in range(N):\n",
    "        D[i,i] = np.sum(W[i])\n",
    "\n",
    "    D_inv = np.linalg.inv(D)\n",
    "    L = D - W\n",
    "    eig_val, eig_vec = np.linalg.eig(np.dot(D_inv, L))\n",
    "\n",
    "    sort_index_ = np.argsort(eig_val)\n",
    "\n",
    "    eig_val = eig_val[sort_index_]\n",
    "    print(\"eig_val[:10]: \", eig_val[:10])\n",
    "\n",
    "    j = 0\n",
    "    while eig_val[j] < 1e-6:\n",
    "        j+=1\n",
    "\n",
    "    print(\"j: \", j)\n",
    "\n",
    "    sort_index_ = sort_index_[j:j+n_dims]\n",
    "    eig_val_picked = eig_val[j:j+n_dims]\n",
    "    print(eig_val_picked)\n",
    "    eig_vec_picked = eig_vec[:, sort_index_]\n",
    "\n",
    "    # print(\"L: \")\n",
    "    # print(np.dot(np.dot(eig_vec_picked.T, L), eig_vec_picked))\n",
    "    # print(\"D: \")\n",
    "    # D not equal I ???\n",
    "    print(np.dot(np.dot(eig_vec_picked.T, D), eig_vec_picked))\n",
    "\n",
    "    X_ndim = eig_vec_picked\n",
    "    return X_ndim\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # X, Y = make_swiss_roll(n_samples = 2000)\n",
    "    # X_ndim = le(X, n_neighbors = 5, t = 20)\n",
    "    #\n",
    "    # fig = plt.figure(figsize=(12,6))\n",
    "    # ax1 = fig.add_subplot(121, projection='3d')\n",
    "    # ax1.scatter(X[:, 0], X[:, 1], X[:, 2], c = Y)\n",
    "    #\n",
    "    # ax2 = fig.add_subplot(122)\n",
    "    # ax2.scatter(X_ndim[:, 0], X_ndim[:, 1], c = Y)\n",
    "    # plt.show()\n",
    "\n",
    "    X = load_digits().data\n",
    "    y = load_digits().target\n",
    "\n",
    "    dist = cal_pairwise_dist(X)\n",
    "    max_dist = np.max(dist)\n",
    "    print(\"max_dist\", max_dist)\n",
    "    X_ndim = le(X, n_neighbors = 20, t = max_dist*0.1)\n",
    "    plt.scatter(X_ndim[:, 0], X_ndim[:, 1], c = y)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# LLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_dist 5935.0\n",
      "eig_val[:10]:  [-9.06393016e-17  2.91631146e-03  6.65082203e-03  9.90821980e-03\n",
      "  1.27832277e-02  1.33949749e-02  2.31575990e-02  2.53186872e-02\n",
      "  3.87587872e-02  4.51036101e-02]\n",
      "j:  1\n",
      "[0.00291631 0.00665082]\n",
      "[[ 1.40711219e+01 -1.99180407e-14]\n",
      " [-1.99120234e-14  1.26249455e+01]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277d5395fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding:utf-8\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "'''\n",
    "author: heucoder\n",
    "email: 812860165@qq.com\n",
    "date: 2019.6.13\n",
    "'''\n",
    "\n",
    "def make_swiss_roll(n_samples=100, noise=0.0, random_state=None):\n",
    "    #Generate a swiss roll dataset.\n",
    "    t = 1.5 * np.pi * (1 + 2 * np.random.rand(1, n_samples))\n",
    "    x = t * np.cos(t)\n",
    "    y = 83 * np.random.rand(1, n_samples)\n",
    "    z = t * np.sin(t)\n",
    "    X = np.concatenate((x, y, z))\n",
    "    X += noise * np.random.randn(3, n_samples)\n",
    "    X = X.T\n",
    "    t = np.squeeze(t)\n",
    "    return X, t\n",
    "\n",
    "def rbf(dist, t = 1.0):\n",
    "    '''\n",
    "    rbf kernel function\n",
    "    '''\n",
    "    return np.exp(-(dist/t))\n",
    "\n",
    "def cal_pairwise_dist(x):\n",
    "\n",
    "    '''计算pairwise 距离, x是matrix\n",
    "    (a-b)^2 = a^2 + b^2 - 2*a*b\n",
    "    '''\n",
    "    sum_x = np.sum(np.square(x), 1)\n",
    "    dist = np.add(np.add(-2 * np.dot(x, x.T), sum_x).T, sum_x)\n",
    "    #返回任意两个点之间距离的平方\n",
    "    return dist\n",
    "\n",
    "def cal_rbf_dist(data, n_neighbors = 10, t = 1):\n",
    "\n",
    "    dist = cal_pairwise_dist(data)\n",
    "    n = dist.shape[0]\n",
    "    rbf_dist = rbf(dist, t)\n",
    "\n",
    "    W = np.zeros((n, n))\n",
    "    for i in range(n):\n",
    "        index_ = np.argsort(dist[i])[1:1+n_neighbors]\n",
    "        W[i, index_] = rbf_dist[i, index_]\n",
    "        W[index_, i] = rbf_dist[index_, i]\n",
    "\n",
    "    return W\n",
    "\n",
    "def le(data,\n",
    "          n_dims = 2,\n",
    "          n_neighbors = 5, t = 1.0):\n",
    "    '''\n",
    "\n",
    "    :param data: (n_samples, n_features)\n",
    "    :param n_dims: target dim\n",
    "    :param n_neighbors: k nearest neighbors\n",
    "    :param t: a param for rbf\n",
    "    :return:\n",
    "    '''\n",
    "    N = data.shape[0]\n",
    "    W = cal_rbf_dist(data, n_neighbors, t)\n",
    "    D = np.zeros_like(W)\n",
    "    for i in range(N):\n",
    "        D[i,i] = np.sum(W[i])\n",
    "\n",
    "    D_inv = np.linalg.inv(D)\n",
    "    L = D - W\n",
    "    eig_val, eig_vec = np.linalg.eig(np.dot(D_inv, L))\n",
    "\n",
    "    sort_index_ = np.argsort(eig_val)\n",
    "\n",
    "    eig_val = eig_val[sort_index_]\n",
    "    print(\"eig_val[:10]: \", eig_val[:10])\n",
    "\n",
    "    j = 0\n",
    "    while eig_val[j] < 1e-6:\n",
    "        j+=1\n",
    "\n",
    "    print(\"j: \", j)\n",
    "\n",
    "    sort_index_ = sort_index_[j:j+n_dims]\n",
    "    eig_val_picked = eig_val[j:j+n_dims]\n",
    "    print(eig_val_picked)\n",
    "    eig_vec_picked = eig_vec[:, sort_index_]\n",
    "\n",
    "    # print(\"L: \")\n",
    "    # print(np.dot(np.dot(eig_vec_picked.T, L), eig_vec_picked))\n",
    "    # print(\"D: \")\n",
    "    # D not equal I ???\n",
    "    print(np.dot(np.dot(eig_vec_picked.T, D), eig_vec_picked))\n",
    "\n",
    "    X_ndim = eig_vec_picked\n",
    "    return X_ndim\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # X, Y = make_swiss_roll(n_samples = 2000)\n",
    "    # X_ndim = le(X, n_neighbors = 5, t = 20)\n",
    "    #\n",
    "    # fig = plt.figure(figsize=(12,6))\n",
    "    # ax1 = fig.add_subplot(121, projection='3d')\n",
    "    # ax1.scatter(X[:, 0], X[:, 1], X[:, 2], c = Y)\n",
    "    #\n",
    "    # ax2 = fig.add_subplot(122)\n",
    "    # ax2.scatter(X_ndim[:, 0], X_ndim[:, 1], c = Y)\n",
    "    # plt.show()\n",
    "\n",
    "    X = load_digits().data\n",
    "    y = load_digits().target\n",
    "\n",
    "    dist = cal_pairwise_dist(X)\n",
    "    max_dist = np.max(dist)\n",
    "    print(\"max_dist\", max_dist)\n",
    "    X_ndim = le(X, n_neighbors = 20, t = max_dist*0.1)\n",
    "    plt.scatter(X_ndim[:, 0], X_ndim[:, 1], c = y)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# LPP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_dist 5935.0\n",
      "eig_val[:10] [0.         0.         0.         0.00174574 0.00860943 0.01745835\n",
      " 0.02143826 0.03495575 0.04032973 0.04504305]\n",
      "j:  3\n",
      "[0.00174574 0.00860943]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277d5210e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding:utf-8\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.datasets import load_digits, load_iris\n",
    "\n",
    "'''\n",
    "author: heucoder\n",
    "email: 812860165@qq.com\n",
    "date: 2019.6.13\n",
    "'''\n",
    "\n",
    "def make_swiss_roll(n_samples=100, noise=0.0, random_state=None):\n",
    "    #Generate a swiss roll dataset.\n",
    "    t = 1.5 * np.pi * (1 + 2 * np.random.rand(1, n_samples))\n",
    "    x = t * np.cos(t)\n",
    "    y = 83 * np.random.rand(1, n_samples)\n",
    "    z = t * np.sin(t)\n",
    "    X = np.concatenate((x, y, z))\n",
    "    X += noise * np.random.randn(3, n_samples)\n",
    "    X = X.T\n",
    "    t = np.squeeze(t)\n",
    "    return X, t\n",
    "\n",
    "def rbf(dist, t = 1.0):\n",
    "    '''\n",
    "    rbf kernel function\n",
    "    '''\n",
    "    return np.exp(-(dist/t))\n",
    "\n",
    "def cal_pairwise_dist(x):\n",
    "\n",
    "    '''计算pairwise 距离, x是matrix\n",
    "    (a-b)^2 = a^2 + b^2 - 2*a*b\n",
    "    '''\n",
    "    sum_x = np.sum(np.square(x), 1)\n",
    "    dist = np.add(np.add(-2 * np.dot(x, x.T), sum_x).T, sum_x)\n",
    "    #返回任意两个点之间距离的平方\n",
    "    return dist\n",
    "\n",
    "def cal_rbf_dist(data, n_neighbors = 10, t = 1):\n",
    "\n",
    "    dist = cal_pairwise_dist(data)\n",
    "    n = dist.shape[0]\n",
    "    rbf_dist = rbf(dist, t)\n",
    "\n",
    "    W = np.zeros((n, n))\n",
    "    for i in range(n):\n",
    "        index_ = np.argsort(dist[i])[1:1 + n_neighbors]\n",
    "        W[i, index_] = rbf_dist[i, index_]\n",
    "        W[index_, i] = rbf_dist[index_, i]\n",
    "\n",
    "    return W\n",
    "\n",
    "def lpp(data,\n",
    "        n_dims = 2,\n",
    "        n_neighbors = 30, t = 1.0):\n",
    "    '''\n",
    "\n",
    "    :param data: (n_samples, n_features)\n",
    "    :param n_dims: target dim\n",
    "    :param n_neighbors: k nearest neighbors\n",
    "    :param t: a param for rbf\n",
    "    :return:\n",
    "    '''\n",
    "    N = data.shape[0]\n",
    "    W = cal_rbf_dist(data, n_neighbors, t)\n",
    "    D = np.zeros_like(W)\n",
    "\n",
    "    for i in range(N):\n",
    "        D[i,i] = np.sum(W[i])\n",
    "\n",
    "    L = D - W\n",
    "    XDXT = np.dot(np.dot(data.T, D), data)\n",
    "    XLXT = np.dot(np.dot(data.T, L), data)\n",
    "\n",
    "    eig_val, eig_vec = np.linalg.eig(np.dot(np.linalg.pinv(XDXT), XLXT))\n",
    "\n",
    "    sort_index_ = np.argsort(np.abs(eig_val))\n",
    "    eig_val = eig_val[sort_index_]\n",
    "    print(\"eig_val[:10]\", eig_val[:10])\n",
    "\n",
    "    j = 0\n",
    "    while eig_val[j] < 1e-6:\n",
    "        j+=1\n",
    "\n",
    "    print(\"j: \", j)\n",
    "\n",
    "    sort_index_ = sort_index_[j:j+n_dims]\n",
    "    # print(sort_index_)\n",
    "    eig_val_picked = eig_val[j:j+n_dims]\n",
    "    print(eig_val_picked)\n",
    "    eig_vec_picked = eig_vec[:, sort_index_]\n",
    "\n",
    "    data_ndim = np.dot(data, eig_vec_picked)\n",
    "\n",
    "    return data_ndim\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    X = load_digits().data\n",
    "    y = load_digits().target\n",
    "    # X, y = make_swiss_roll(n_samples = 1000)\n",
    "\n",
    "    dist = cal_pairwise_dist(X)\n",
    "    max_dist = np.max(dist)\n",
    "    print(\"max_dist\", max_dist)\n",
    "\n",
    "    data_2d = lpp(X, n_neighbors = 5, t = 0.01*max_dist)\n",
    "    data_2 = PCA(n_components=2).fit_transform(X)\n",
    "\n",
    "\n",
    "    plt.figure(figsize=(12,6))\n",
    "    plt.subplot(121)\n",
    "    plt.title(\"LPP\")\n",
    "    plt.scatter(data_2d[:, 0], data_2d[:, 1], c = y)\n",
    "\n",
    "    plt.subplot(122)\n",
    "    plt.title(\"PCA\")\n",
    "    plt.scatter(data_2[:, 0], data_2[:, 1], c = y)\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\numpy\\core\\numeric.py:553: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277d5c34320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding:utf-8\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.manifold import MDS\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "'''\n",
    "author: heucoder\n",
    "email: 812860165@qq.com\n",
    "date: 2019.6.13\n",
    "'''\n",
    "\n",
    "def cal_pairwise_dist(x):\n",
    "    '''计算pairwise 距离, x是matrix\n",
    "    (a-b)^2 = a^2 + b^2 - 2*a*b\n",
    "    '''\n",
    "    sum_x = np.sum(np.square(x), 1)\n",
    "    dist = np.add(np.add(-2 * np.dot(x, x.T), sum_x).T, sum_x)\n",
    "    #返回任意两个点之间距离的平方\n",
    "    return dist\n",
    "\n",
    "\n",
    "def my_mds(data, n_dims):\n",
    "    '''\n",
    "\n",
    "    :param data: (n_samples, n_features)\n",
    "    :param n_dims: target n_dims\n",
    "    :return: (n_samples, n_dims)\n",
    "    '''\n",
    "\n",
    "    n, d = data.shape\n",
    "    dist = cal_pairwise_dist(data)\n",
    "    T1 = np.ones((n,n))*np.sum(dist)/n**2\n",
    "    T2 = np.sum(dist, axis = 1, keepdims=True)/n\n",
    "    T3 = np.sum(dist, axis = 0, keepdims=True)/n\n",
    "\n",
    "    B = -(T1 - T2 - T3 + dist)/2\n",
    "\n",
    "    eig_val, eig_vector = np.linalg.eig(B)\n",
    "    index_ = np.argsort(-eig_val)[:n_dims]\n",
    "    picked_eig_val = eig_val[index_].real\n",
    "    picked_eig_vector = eig_vector[:, index_]\n",
    "    # print(picked_eig_vector.shape, picked_eig_val.shape)\n",
    "    return picked_eig_vector*picked_eig_val**(0.5)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    iris = load_iris()\n",
    "    data = iris.data\n",
    "    Y = iris.target\n",
    "    data_1 = my_mds(data, 2)\n",
    "\n",
    "    data_2 = MDS(n_components=2).fit_transform(data)\n",
    "\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.subplot(121)\n",
    "    plt.title(\"my_MDS\")\n",
    "    plt.scatter(data_1[:, 0], data_1[:, 1], c=Y)\n",
    "\n",
    "    plt.subplot(122)\n",
    "    plt.title(\"sklearn_MDS\")\n",
    "    plt.scatter(data_2[:, 0], data_2[:, 1], c=Y)\n",
    "    plt.savefig(\"MDS_1.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277d52db710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding:utf-8\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "'''\n",
    "author: heucoder\n",
    "email: 812860165@qq.com\n",
    "date: 2019.6.13\n",
    "'''\n",
    "\n",
    "def pca(data, n_dim):\n",
    "    '''\n",
    "\n",
    "    pca is O(D^3)\n",
    "    :param data: (n_samples, n_features(D))\n",
    "    :param n_dim: target dimensions\n",
    "    :return: (n_samples, n_dim)\n",
    "    '''\n",
    "    data = data - np.mean(data, axis = 0, keepdims = True)\n",
    "\n",
    "    cov = np.dot(data.T, data)\n",
    "\n",
    "    eig_values, eig_vector = np.linalg.eig(cov)\n",
    "    # print(eig_values)\n",
    "    indexs_ = np.argsort(-eig_values)[:n_dim]\n",
    "    picked_eig_values = eig_values[indexs_]\n",
    "    picked_eig_vector = eig_vector[:, indexs_]\n",
    "    data_ndim = np.dot(data, picked_eig_vector)\n",
    "    return data_ndim\n",
    "\n",
    "\n",
    "# data 降维的矩阵(n_samples, n_features)\n",
    "# n_dim 目标维度\n",
    "# fit n_features >> n_samples, reduce cal\n",
    "def highdim_pca(data, n_dim):\n",
    "    '''\n",
    "\n",
    "    when n_features(D) >> n_samples(N), highdim_pca is O(N^3)\n",
    "\n",
    "    :param data: (n_samples, n_features)\n",
    "    :param n_dim: target dimensions\n",
    "    :return: (n_samples, n_dim)\n",
    "    '''\n",
    "    N = data.shape[0]\n",
    "    data = data - np.mean(data, axis = 0, keepdims = True)\n",
    "\n",
    "    Ncov = np.dot(data, data.T)\n",
    "\n",
    "    Neig_values, Neig_vector = np.linalg.eig(Ncov)\n",
    "    indexs_ = np.argsort(-Neig_values)[:n_dim]\n",
    "    Npicked_eig_values = Neig_values[indexs_]\n",
    "    # print(Npicked_eig_values)\n",
    "    Npicked_eig_vector = Neig_vector[:, indexs_]\n",
    "    # print(Npicked_eig_vector.shape)\n",
    "\n",
    "    picked_eig_vector = np.dot(data.T, Npicked_eig_vector)\n",
    "    picked_eig_vector = picked_eig_vector/(N*Npicked_eig_values.reshape(-1, n_dim))**0.5\n",
    "    # print(picked_eig_vector.shape)\n",
    "\n",
    "    data_ndim = np.dot(data, picked_eig_vector)\n",
    "    return data_ndim\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    data = load_iris()\n",
    "    X = data.data\n",
    "    Y = data.target\n",
    "    data_2d1 = pca(X, 2)\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.subplot(121)\n",
    "    plt.title(\"my_PCA\")\n",
    "    plt.scatter(data_2d1[:, 0], data_2d1[:, 1], c = Y)\n",
    "\n",
    "    sklearn_pca = PCA(n_components=2)\n",
    "    data_2d2 = sklearn_pca.fit_transform(X)\n",
    "    plt.subplot(122)\n",
    "    plt.title(\"sklearn_PCA\")\n",
    "    plt.scatter(data_2d2[:, 0], data_2d2[:, 1], c = Y)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# KPCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.84696253 5.58075796]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277d52eb8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding:utf-8\n",
    "# 实现KPCA\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.decomposition import KernelPCA\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "\n",
    "'''\n",
    "author: heucoder\n",
    "email: 812860165@qq.com\n",
    "date: 2019.6.13\n",
    "'''\n",
    "\n",
    "\n",
    "def sigmoid(x, coef = 0.25):\n",
    "    x = np.dot(x, x.T)\n",
    "    return np.tanh(coef*x+1)\n",
    "\n",
    "def linear(x):\n",
    "    x = np.dot(x, x.T)\n",
    "    return x\n",
    "\n",
    "def rbf(x, gamma = 15):\n",
    "    sq_dists = pdist(x, 'sqeuclidean')\n",
    "    mat_sq_dists = squareform(sq_dists)\n",
    "    return np.exp(-gamma*mat_sq_dists)\n",
    "\n",
    "def kpca(data, n_dims=2, kernel = rbf):\n",
    "    '''\n",
    "\n",
    "    :param data: (n_samples, n_features)\n",
    "    :param n_dims: target n_dims\n",
    "    :param kernel: kernel functions\n",
    "    :return: (n_samples, n_dims)\n",
    "    '''\n",
    "\n",
    "    K = kernel(data)\n",
    "    #\n",
    "    N = K.shape[0]\n",
    "    one_n = np.ones((N, N)) / N\n",
    "    K = K - one_n.dot(K) - K.dot(one_n) + one_n.dot(K).dot(one_n)\n",
    "    #\n",
    "    eig_values, eig_vector = np.linalg.eig(K)\n",
    "    idx = eig_values.argsort()[::-1]\n",
    "    eigval = eig_values[idx][:n_dims]\n",
    "    eigvector = eig_vector[:, idx][:, :n_dims]\n",
    "    print(eigval)\n",
    "    eigval = eigval**(1/2)\n",
    "    vi = eigvector/eigval.reshape(-1,n_dims)\n",
    "    data_n = np.dot(K, vi)\n",
    "    return data_n\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    data = load_iris().data\n",
    "    Y = load_iris().target\n",
    "    data_1 = kpca(data, kernel=rbf)\n",
    "\n",
    "\n",
    "    sklearn_kpca = KernelPCA(n_components=2, kernel=\"rbf\", gamma=15)\n",
    "    data_2 = sklearn_kpca.fit_transform(data)\n",
    "\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.subplot(121)\n",
    "    plt.title(\"my_KPCA\")\n",
    "    plt.scatter(data_1[:, 0], data_1[:, 1], c = Y)\n",
    "\n",
    "    plt.subplot(122)\n",
    "    plt.title(\"sklearn_KPCA\")\n",
    "    plt.scatter(data_2[:, 0], data_2[:, 1], c = Y)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# coding:utf-8\n",
    "\n",
    "'''\n",
    "author: heucoder\n",
    "email: 812860165@qq.com\n",
    "date: 2019.6.13\n",
    "'''\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "def svd(data):\n",
    "    '''\n",
    "    :param data:\n",
    "    :return: U, Sigma, VT\n",
    "    '''\n",
    "\n",
    "    # mean\n",
    "    N, D = data.shape\n",
    "    data = data - np.mean(data, axis=0)\n",
    "\n",
    "    # V\n",
    "    Veig_val, Veig_vector = np.linalg.eigh(np.dot(data.T, data))\n",
    "    VT = Veig_vector[:, np.argsort(-abs(Veig_val))].T\n",
    "\n",
    "    # U\n",
    "    Ueig_val, Ueig_vector = np.linalg.eigh(np.dot(data, data.T))\n",
    "    U = Ueig_vector[:, np.argsort(-abs(Ueig_val))]\n",
    "\n",
    "    # Sigma\n",
    "    Sigma = np.zeros((N, D))\n",
    "    for i in range(D):\n",
    "        Sigma[i, i] = np.dot(data, VT[i])[i]/U[i,i]\n",
    "\n",
    "    return U, Sigma, VT\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    iris = load_iris()\n",
    "    X = iris.data\n",
    "    Y = iris.target\n",
    "    U, Sigma, VT = svd(X)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# T-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing pairwise distances...\n",
      "Computing pair_prob for point 0 of 1797 ...\n",
      "Computing pair_prob for point 500 of 1797 ...\n",
      "Computing pair_prob for point 1000 of 1797 ...\n",
      "Computing pair_prob for point 1500 of 1797 ...\n",
      "Mean value of sigma:  11.698543429042957\n",
      "T-SNE DURING:4.4624768174329335e-07\n",
      "Iteration  100 : error is  13.007440599499583\n",
      "Iteration  200 : error is  0.8795248234818023\n",
      "ratio  0.06761705477368384\n",
      "Iteration  300 : error is  0.7573072812477073\n",
      "ratio  0.8610413953408744\n",
      "Iteration  400 : error is  0.7192437065551013\n",
      "ratio  0.9497382692136618\n",
      "Iteration  500 : error is  0.7018331367508167\n",
      "ratio  0.9757932260712097\n",
      "finished training!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x277d5d13d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding:utf-8\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_digits\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "def cal_pairwise_dist(x):\n",
    "    '''计算pairwise 距离, x是matrix\n",
    "    (a-b)^2 = a^2 + b^2 - 2*a*b\n",
    "    '''\n",
    "    sum_x = np.sum(np.square(x), 1)\n",
    "    dist = np.add(np.add(-2 * np.dot(x, x.T), sum_x).T, sum_x)\n",
    "    #返回任意两个点之间距离的平方\n",
    "    return dist\n",
    "\n",
    "def cal_perplexity(dist, idx=0, beta=1.0):\n",
    "    '''计算perplexity, D是距离向量，\n",
    "    idx指dist中自己与自己距离的位置，beta是高斯分布参数\n",
    "    这里的perp仅计算了熵，方便计算\n",
    "    '''\n",
    "    prob = np.exp(-dist * beta)\n",
    "    # 设置自身prob为0\n",
    "    prob[idx] = 0\n",
    "    sum_prob = np.sum(prob)\n",
    "    if sum_prob < 1e-12:\n",
    "        prob = np.maximum(prob, 1e-12)\n",
    "        perp = -12\n",
    "    else:\n",
    "        perp = np.log(sum_prob) + beta * np.sum(dist * prob) / sum_prob\n",
    "        prob /= sum_prob\n",
    "\n",
    "    return perp, prob\n",
    "\n",
    "def seach_prob(x, tol=1e-5, perplexity=30.0):\n",
    "    '''二分搜索寻找beta,并计算pairwise的prob\n",
    "    '''\n",
    "\n",
    "    # 初始化参数\n",
    "    print(\"Computing pairwise distances...\")\n",
    "    (n, d) = x.shape\n",
    "    dist = cal_pairwise_dist(x)\n",
    "    pair_prob = np.zeros((n, n))\n",
    "    beta = np.ones((n, 1))\n",
    "    # 取log，方便后续计算\n",
    "    base_perp = np.log(perplexity)\n",
    "\n",
    "    for i in range(n):\n",
    "        if i % 500 == 0:\n",
    "            print(\"Computing pair_prob for point %s of %s ...\" %(i,n))\n",
    "\n",
    "        betamin = -np.inf\n",
    "        betamax = np.inf\n",
    "        perp, this_prob = cal_perplexity(dist[i], i, beta[i])\n",
    "\n",
    "        # 二分搜索,寻找最佳sigma下的prob\n",
    "        perp_diff = perp - base_perp\n",
    "        tries = 0\n",
    "        while np.abs(perp_diff) > tol and tries < 50:\n",
    "            if perp_diff > 0:\n",
    "                betamin = beta[i].copy()\n",
    "                if betamax == np.inf or betamax == -np.inf:\n",
    "                    beta[i] = beta[i] * 2\n",
    "                else:\n",
    "                    beta[i] = (beta[i] + betamax) / 2\n",
    "            else:\n",
    "                betamax = beta[i].copy()\n",
    "                if betamin == np.inf or betamin == -np.inf:\n",
    "                    beta[i] = beta[i] / 2\n",
    "                else:\n",
    "                    beta[i] = (beta[i] + betamin) / 2\n",
    "\n",
    "            # 更新perb,prob值\n",
    "            perp, this_prob = cal_perplexity(dist[i], i, beta[i])\n",
    "            perp_diff = perp - base_perp\n",
    "            tries = tries + 1\n",
    "        # 记录prob值\n",
    "        pair_prob[i,] = this_prob\n",
    "    print(\"Mean value of sigma: \", np.mean(np.sqrt(1 / beta)))\n",
    "    #每个点对其他点的条件概率分布pi\\j\n",
    "    return pair_prob\n",
    "\n",
    "def tsne(x, no_dims=2, perplexity=30.0, max_iter=1000):\n",
    "    \"\"\"Runs t-SNE on the dataset in the NxD array x\n",
    "    to reduce its dimensionality to no_dims dimensions.\n",
    "    The syntaxis of the function is Y = tsne.tsne(x, no_dims, perplexity),\n",
    "    where x is an NxD NumPy array.\n",
    "    \"\"\"\n",
    "\n",
    "    # Check inputs\n",
    "    if isinstance(no_dims, float):\n",
    "        print(\"Error: array x should have type float.\")\n",
    "        return -1\n",
    "\n",
    "    (n, d) = x.shape\n",
    "\n",
    "    # 动量\n",
    "    initial_momentum = 0.5\n",
    "    final_momentum = 0.8\n",
    "    eta = 500\n",
    "    min_gain = 0.01\n",
    "    # 随机初始化Y\n",
    "    y = np.random.randn(n, no_dims)\n",
    "    # dy梯度\n",
    "    dy = np.zeros((n, no_dims))\n",
    "    # iy是什么\n",
    "    iy = np.zeros((n, no_dims))\n",
    "\n",
    "    gains = np.ones((n, no_dims))\n",
    "\n",
    "    # 对称化\n",
    "    P = seach_prob(x, 1e-5, perplexity)\n",
    "    P = P + np.transpose(P)\n",
    "    P = P / np.sum(P)   #pij\n",
    "    # early exaggeration\n",
    "    # pi\\j，提前夸大\n",
    "    print (\"T-SNE DURING:%s\" % time.clock())\n",
    "    P = P * 4\n",
    "    P = np.maximum(P, 1e-12)\n",
    "\n",
    "    # Run iterations\n",
    "    for iter in range(max_iter):\n",
    "        # Compute pairwise affinities\n",
    "        sum_y = np.sum(np.square(y), 1)\n",
    "        num = 1 / (1 + np.add(np.add(-2 * np.dot(y, y.T), sum_y).T, sum_y))\n",
    "        num[range(n), range(n)] = 0\n",
    "        Q = num / np.sum(num)   #qij\n",
    "        Q = np.maximum(Q, 1e-12)    #X与Y逐位比较取其大者\n",
    "\n",
    "        # Compute gradient\n",
    "        # np.tile(A,N) 重复数组AN次 [1],5 [1,1,1,1,1]\n",
    "        # pij-qij\n",
    "        PQ = P - Q\n",
    "        # 梯度dy\n",
    "        for i in range(n):\n",
    "            dy[i,:] = np.sum(np.tile(PQ[:,i] * num[:,i], (no_dims, 1)).T * (y[i,:] - y), 0)\n",
    "\n",
    "        # Perform the update\n",
    "        if iter < 20:\n",
    "            momentum = initial_momentum\n",
    "        else:\n",
    "            momentum = final_momentum\n",
    "\n",
    "        gains = (gains + 0.2) * ((dy > 0) != (iy > 0)) + (gains * 0.8) * ((dy > 0) == (iy > 0))\n",
    "        gains[gains < min_gain] = min_gain\n",
    "        # 迭代\n",
    "        iy = momentum * iy - eta * (gains * dy)\n",
    "        y = y + iy\n",
    "        y = y - np.tile(np.mean(y, 0), (n, 1))\n",
    "        # Compute current value of cost function\\\n",
    "        if (iter + 1) % 100 == 0:\n",
    "            C = np.sum(P * np.log(P / Q))\n",
    "            print(\"Iteration \", (iter + 1), \": error is \", C)\n",
    "            if (iter+1) != 100:\n",
    "                ratio = C/oldC\n",
    "                print(\"ratio \", ratio)\n",
    "                if ratio >= 0.95:\n",
    "                    break\n",
    "            oldC = C\n",
    "        # Stop lying about P-values\n",
    "        if iter == 100:\n",
    "            P = P / 4\n",
    "    print(\"finished training!\")\n",
    "    return y\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    digits = load_digits()\n",
    "    X = digits.data\n",
    "    Y = digits.target\n",
    "\n",
    "    data_2d = tsne(X, 2)\n",
    "    plt.scatter(data_2d[:, 0], data_2d[:, 1], c = Y)\n",
    "    plt.show()\n"
   ]
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